Planetary gearbox fault diagnosis using an adaptive stochastic resonance method
Planetary gearbox fault diagnosis using an adaptive stochastic resonance method
复制标题
采用自适应随机共振法的行星齿轮箱故障诊断
DOI：
10.1016/j.ymssp.2012.06.021
复制
发表时间：
2013-07-05
影响因子：
8.4
通讯作者：
He, Zhengjia
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;Han, Dong;He, Zhengjia
行星齿轮箱因其传动比大、承载能力强、传动效率高而广泛应用于航空航天、汽车和重工业应用。重载和强烈冲击载荷的恶劣运行条件可能会导致轮齿损坏，如疲劳裂纹和缺齿等。行星齿轮箱故障诊断的挑战性问题包括敏感测量位置的选择、振动传播路径的研究和弱特征提取。其中之一是如何从行星齿轮箱故障部件的噪声信号中有效地发现微弱特征。针对行星齿轮箱故障诊断问题，本文提出了一种自适应随机共振（ASR）方法。 ASR方法利用蚁群算法的优化能力，自适应地实现与输入信号匹配的最优随机共振系统。采用ASR方法可以减弱噪声，突出微弱特征，从而可以准确诊断故障。建立了行星齿轮箱试验台，对太阳轮缺齿、缺齿等故障进行了实验。并在负载条件和各种电机速度下收集振动信号。采用该方法对采集到的信号进行处理，特征提取和故障诊断结果验证了该方法的有效性。 (C) 2012 Elsevier Ltd. 保留所有权利。
Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness. (C) 2012 Elsevier Ltd. All rights reserved.